import pickle
from sklearn import manifold
import numpy as np
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot, iplot_mpl
from plotly.graph_objs import *
init_notebook_mode()
layout = Layout(
autosize=False,
width=600,
height=600,
xaxis=dict(
autorange=True,
showgrid=False,
zeroline=False,
autotick=True,
showline=True,
mirror='all'
),
yaxis=dict(
autorange=True,
showgrid=False,
zeroline=False,
autotick=True,
showline=True,
mirror='all'
)
)
with open("models.pickle", 'rb') as f:
models = pickle.load(f)
model = models[0].model
weights = model.layers[0].get_weights()
store_embedding = weights[0]
dow_embedding = weights[1]
year_embedding = weights[4]
month_embedding = weights[5]
day_embedding = weights[6]
german_states_embedding = weights[20]
woy_embedding = weights[21]
weather_event_embedding = weights[30]
tsne = manifold.TSNE(init='pca', random_state=0, method='exact')
Y = tsne.fit_transform(german_states_embedding)
names = ['Niedersachsen', 'Hamburg', 'Thueringen', 'RheinlandPfalz', 'SachsenAnhalt', 'BadenWuerttemberg','Sachsen', 'Berlin', 'Hessen', 'SchleswigHolstein', 'Bayern', 'NordrheinWestfalen']
trace1 = Scatter(
x=-Y[:, 0],
y=-Y[:, 1],
mode='markers+text',
text=names,
textposition='top'
)
fig = Figure(data=[trace1], layout=layout)
iplot(fig)
tsne = manifold.TSNE(init='pca', random_state=0, method='exact')
Y = tsne.fit_transform(dow_embedding)
names = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat','Sun']
trace1 = Scatter(
x=-Y[:, 0],
y=-Y[:, 1],
mode='markers+text',
text=names,
textposition='top'
)
fig = Figure(data=[trace1], layout=layout)
iplot(fig)
tsne = manifold.TSNE(init='pca', random_state=0, method='exact')
Y = tsne.fit_transform(month_embedding)
names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
trace1 = Scatter(
x=-Y[:, 0],
y=-Y[:, 1],
mode='markers+text',
text=names,
textposition='top'
)
fig = Figure(data=[trace1], layout=layout)
iplot(fig)
tsne = manifold.TSNE(init='pca', random_state=0, method='exact', learning_rate=500, verbose=0, early_exaggeration=1)
Y = tsne.fit_transform(store_embedding)
names = [str(i) for i in range(1, 1116)]
layout = Layout(
autosize=False,
width=800,
height=800,
xaxis=dict(
autorange=True,
showgrid=False,
zeroline=False,
autotick=True,
showline=True,
mirror='all'
),
yaxis=dict(
autorange=True,
showgrid=False,
zeroline=False,
autotick=True,
showline=True,
mirror='all'
)
)
trace1 = Scatter(
x=Y[:, 0],
y=Y[:, 1],
mode='markers',
text=names,
textposition='top'
)
fig = Figure(data=[trace1], layout=layout)
iplot(fig)
tsne = manifold.TSNE(n_components=3, init='pca', random_state=0, method='exact', learning_rate=500, verbose=0, early_exaggeration=1)
Y = tsne.fit_transform(store_embedding)
names = [str(i) for i in range(1, 1116)]
layout = Layout(
autosize=False,
width=1000,
height=1000,
)
trace1 = Scatter3d(
x=Y[:, 0],
y=Y[:, 1],
z=Y[:, 2],
mode='markers',
text=names,
marker=dict(
size=2,
opacity=0.9
)
)
fig = Figure(data=[trace1], layout=layout)
iplot(fig)